The year 2022 will perhaps be remembered as the year when artificial intelligence (AI) really became common property. Many have already tested programs such as DALL-E, Midjourney, Stable Diffusion, Galactica and ChatGPT. These are examples of two main types of programs that have attracted a lot of attention recently, and both are variants of what we can call generative artificial intelligence.
The first type of programs can create new images or illustrations from a text prompt, while the other can correspondingly create new text, such as an answer to a question or as a continuation of an introductory text. The special thing is that both the images and texts generated are completely new and not direct copies of other images or texts.
The AI algorithms are trained by being fed large amounts of images and texts that the developers have access to, preferably through their own web-platforms, such as Google, Facebook and Instagram. The algorithms have learned to respond to the textual input by combining elements, such as motifs and words, which often appear together in the training examples. The algorithms are very good at recognizing statistical patterns in large amounts of data and use this when new images or texts are to be generated.
The new programs have been received with everything from great enthusiasm by some, to great concern and skepticism by others. The strong emotions shown may reflect a belief that we are now facing a new dimension in AI, namely a kind of Artificial Creativity. In that case, this would be a radical step forward for AI. Could that be the case? Headlines like CNN’s «AI won an art contest, and artists are furious» is an example that shows that this is the impression out there, and that this has the potential to change the creative professions.
Creativity, no longer exclusive to humans?
Right from the start of the 2nd industrial revolution around 1870, through the transition to the digital society around 1970, and until the last decade’s strong rise in artificial intelligence, the fear that the machines will take our jobs away has been there. And many tasks have also disappeared through automation of the most routine jobs in this period. Through all these phases of industrial and technological development, there have nevertheless been human qualities that almost no one thought the machines could replace, and one of these is our ability to be creative. Creativity has, in a sense, been man’s safe haven against the systematic operationality of machines and algorithms.
Creativity is also an important component in many types of work, such as of course in art, music and poetry, but also in marketing, design, innovation and research. It is precisely this certainty about the limitations of machines in the creative field that is now being shaken by the new algorithms.
And this concern is now being expressed on many fronts. Artists and illustrators worldwide are now showing their great skepticism. One thing is that the algorithms create wonderful images and illustrations, but questionably it is done on the basis of their art and without this being credited or compensated. Another thing is that now anyone can create high-quality illustrations for their own use almost free of charge without engaging graphic artists and illustrators. The fear for one’s own livelihood and the feeling of exploitation of one’s own art recently led to a “NO TO AI GENERATED IMAGES” movement among artists on the online community ArtStation. Here they demand that their art should not be included in the training of the AI algorithms.
Protection of one’s own intellectual property was recently also the basis for a lawsuit in a quite different field, namely in computer programming. In November, a group of programmers filed a lawsuit against the companies GitHub, Microsoft and OpenAI for the way their algorithms exploit their and other people’s software to train their helper programs. These algorithms can generate ready data code to solve desired programming tasks. These co-pilots, as the AI programs are called, have thus been trained on large amounts of openly available program code created by contributors worldwide and used without their consent. Here, too, we see how men’s creative abilities are threatened.
Research is also a creative profession. The search for new knowledge requires new thinking and exploration of unknown territory. The company Meta, which owns Facebook, recently published the service Galactica, an AI generator that should help researchers to summarize available research in a field, or to generate text for scientific publications. The texts that the program generated were apparently impressive, but received massive criticism from researchers worldwide. It turned out that Galactica to a large extent, and with the greatest conviction, “hallucinated” both scientific results and references to other research. Galactica was removed from the network after three days, at least temporarily. Such hallucinatory properties also characterize the latest news in text generation, namely ChatGPT, which many have played with in recent weeks, and which is already creating challenges within the education system.
The example from research is important in terms of answering the question of whether we have now witnessed the rise of artificial creativity. But, what does it really mean to be creative? Is it sufficient to simply be able to create something new?
As we saw from Galactica’s troublesome launch, the answer is probably no. Something more is required.
There are typically two elements that are included in different definitions of creativity. A creative act must carry with it both an element of being “new”, but at the same time be considered “useful”. It was on this last point that Meta’s product did not quite measure up.
The texts that Galactica created were not considered useful enough. On the contrary. Meta received massive criticism for facilitating potential mass dissemination of “fake science”. Almost anyone could now easily create convincing «research articles» without substance and with erroneous conclusions and spread this in a suitable way, for instance through open archives.
The importance of biases
All the programs that have been mentioned here will to some extent fulfill the requirement of creating something new, but to varying degrees they fulfill the second requirement of creating something useful.
In the latter of my series of blog posts on creativity (Bending, Blending and Breaking biases) I argue that a key to human creativity lies in our cognitive biases. This is so because every new thought aspiring to be deemed creative must be evaluated with regard to its usefulness within some reference frame or bias structure. It is common to use the phrase “thinking outside the box” to describe the process of coming up with new and unconventional thoughts that may lead to creative ideas, artwork or scientific discoveries. However, I believe this is turning creativity inside-out, literally. Creativity is always to think inside some box, because without a box (a bias, or a frame of reference) the usefulness of a novel thought cannot be evaluated!
In the same blog post I therefore claim that: the true act of creativity is the restructuring of intrinsic structures, biases or reference frames, with the purpose of bringing meaning to novelty.
This restructuring or change of biases is a top-down cognitive process by which we change the way we perceive the world. Basically we apply or create another «box» for our thinking.
Why is this so important in relation to artificial creativity? Well, it is the case that none of the usual algorithms used in artificial intelligence today are able to make such top-down assessment of usefulness of novelties, either of incoming perceptions or self-generated novelties. In short, the algorithms have no idea what they are seeing or creating. They have no top-down bias structure providing the tool for assessing what is normal or acceptable within old or new reference frames.
They are also unable to understand causal relationships, and thus cannot carry out thought experiments, which is important to be able to assess how a new idea or product will work if they were realized. Likewise, Galactica could not assess whether text, theories, formulas or references that it generated for scientific purposes, were actually in line with previous knowledge or basic axioms within a field.(See this post by G. Marcus for more discussion on the limitations of the type of models underlying ChatGPT and Galactica)
Generative AI and types of creativity
So does this mean that artificial intelligence is in no way creative? In order to get closer to an answer to this question we need to view the generative algorithms in light of different types of creativity.
In his research together with professor and composer Anthony Brandt at Rice University, brain researcher David Eagleman at Stanford University has found that most creative expressions created by humans can be placed in one of three main categories. They argue that human creativity can be described by the way it springs from existing ideas or products. It can happen either by bending, blending or breaking. Here I build further on their work as it can be useful when we have to decide on the existence of artificial creativity.
- In the case of bending, creativity is unleashed through small adjustments to ideas or products that are already considered useful. A great deal of what is created in the world ends up in this category. All that is created by bending are variations of what is already accepted. AI can create new things by bending existing ideas and products, and in part these will be considered creative since what is created will very likely end up within what is already considered useful. Still, AI itself cannot evaluate this.
- Creativity may alternatively find its outlet by mixing old ideas, that is, by blending, where it takes advantage of transferring concepts from one area to another. Old recipes can be used as solutions to new problems. Within research, this form is probably widespread in that methods and ideas can be applied within new issues or subject areas. This form of creativity requires a more thorough assessment of what can be considered useful or “correct” since one moves between what has been already accepted. The generative AI algorithms, such as DALL-E and Stable Diffusion, create their illustrations and images precisely through blending of images that it has learned from. However, they still fail to be able to consider their own creations as beautiful, harmonious, or useful in any sense whatsoever.
- Thirdly, creativity through breaking often leads to revolutionary new designs through the disruption of established structures and through the building-up of new order. Since here one is moving completely outside of what was previously generally valid and accepted, it can be difficult and time-consuming to assert the usefulness. Anyone who succeeds with this type of creativity often appears as pioneering or brilliant in retrospect. Examples are Einstein with his theories of relativity, which were violations of previous “truths” in science, and Picasso, who presented innovative stylistic directions in the art of painting. As of today, there are probably no AI-algorithms capable of creating such new structures or orders that break radically with the input with which they have been trained, and of course they are also unable to assess the usefulness of such creations.
We can thus summarize the creative characteristics of AI algorithms in 2022 as follows:
|Type of creativity||Can generate novelty||Expected to be useful|
|Bending||Yes||Yes, but unintentionally|
The conclusion that I draw from this is that we have yet to see algorithms that exhibit artificial creativity. Algorithms can in no way replace human creative competence as of today.
However, the new AI algorithms can be considered useful tools that can help increase the exploration of new territory and come up with suggestions for solutions within the categories of bending and blending. For ground-breaking creativity, for instance in research, AI comes short in every respect, though. Whether or not the final creation, be it art, music, innovation or research, can be considered creative, will for now have to be left entirely to the human being who uses these tools.
I started this blog series on creativity asking: Will Apple Siri ever shout Eureka? It is time to return to this question.
Through five blogs I have built up an argument that AI so far and to some extent is capable of generating novelty, and this is of course a necessary element of any Eureka moment experience. However, I have also argued that today’s algorithms are lacking the top-down ability to evaluate their own creations.
Many developers of AI are trying to figure out how the algorithms can become more flexible in order to be able to handle multi-modal inputs and shifting environments. Some are also working towards creating AI with some kind of human «common sense» and the ability to infer causality from perceptions. We are probably far from achieving this today. I’m convinced that any future solution to this must include a dynamical model with a stronger emphasis on top-down bias feedback loops to balance bottom-up perception. Here one should seek inspiration from human cognition and predictive inference theory.
Nevertheless, if the efforts to develop AI capable of top-down assessment of the usefulness of novelties become reality, it would be a small add-on simply to instruct the program to flag creative discoveries with an «Eureka»! eclamation.
So, yes, Siri, or more likely, her successor, will probably shout Eureka! some day, if we want her to.
However, this was not the kind of Eureka moment that Arkimedes experienced as he junmed out og his bath. The Eureka moment, or the moment of imaginative insight, as I discussed in this blog post, is the moment of sudden conscious awareness of an unconscious creation. The momentary experience of qualia that is accompanying the aha-feeling is still lacking.
Hence, Apple Siri of the future would only shout Eureka! and really «mean it» (!) if she was to become conscious of her own unconsciousness.
But that is another story….